Ever wonder why is Elon Musk and Stephen Hawking are terrified of AI? It is probably because we are not able to anticipate technological changes at the pace at which it is changing. Last year we saw machines defeat humans at the most abstract board game ‘Go’. Machine driven cars is already a reality. The day is not far when machines will replace humans in most processes.
So how can a computer be better than humans? One way to analyse this is the idea of reverse engineering the human brain. Let’s try to figure out the bases on which the brain takes a decision and attempt to represent that logic in the code. Take the example of a doctor.
A sick person visits a doctor for diagnosis. The doctor follows the below procedure to treat the person.
- Observe visible symptoms.
- Identify list of diseases that share the symptoms.
- Do a blood test to further narrow down on diseases.
- Narrow down to the most probable disease.
- Start medication and validate results.
- If results don’t validate given disease, start medication for next most probable disease.
The same workflow can be easily represented by logic:
input(symptoms) If symptoms in disease[symptoms]: Add disease in [possible_diseases] Input(blood_test_result) If blood_test_result not in disease in[possible_diseases] Remove disease from [possible_diseases] X = 0 Start medication(possible_diseases[x]) Return result; while result = null: x++ Start medication(possible_diseases[x])
Now obviously, a doctor identifies diseases using a much more complicated workflow. But in business, we employ humans to take decisions, like project management, resource management, inventory management etc. which are comparatively simpler and the stakes involved may not be life and death. What is running in the market? When and how much stock should be ordered? Or, which employee should do what task? These decisions are taken on the basis of how data is analysed by our brain, collected using our sense organs.
The power of analysing data reduces as the employee gets tired, sick or goes AWOL. Due to which, the efficiency decreases. This is unlikely in case of machines. More and more softwares are being developed regularly to provide better and more consistent results than before. It helps us guide our businesses better and take strategic decisions.
With AI, it would be possible to achieve half a million years of human knowledge in just half a year. Therefore, next 6 months can be transformational based on how its adopted and implemented.
It is still early days, but the impact of AI and machine learning on how companies operate is soon going to skyrocket as their owners realize the capabilities as well as the need for it. Especially, the fear of losing out to a competitor who’s harnessing the power of AI. It’s not too far-fetched for game theory enthusiasts to see that AI replacing humans would be the optimal outcome Nash equilibrium. Meaning, inevitable.
U.S retail market is now capable of accurately predicting when their customers are expected to have a baby. Further, offering discounts to those customers on baby products on e-commerce sites. In a similar example of machine intelligence, Bank of America noticed that it's best performing employees have been the one’s who took breaks together. Conclusively, they instituted group break policies which led to 23% increase in their employees’ performance.
Are you wondering if your business is big enough to actually need machine intelligence? The answer is: Obviously, yes. Given that you’ve come this far in the article.
Even the smallest food vendor can use basic algorithms to identify what stock should be renewed at what intervals on the basis of his sales. Another example can be, the case of one person out of a sales team of four over achieving. We can write algorithms to identify what he talks to the client that gives him better conversion and same can be taught to the other team members.
What is certain is that the rate at which companies add new jobs will slow down considerably. Companies will no longer see high headcount as an asset. They will see it as a liability. Under these conditions, new skills will become very valuable. Cloud computing, Data segmentation, Mobile solutions, Machine learning are going to be the future front of skill requirements.
Every sector of the economy will be affected by AI disruptions. There is no escaping this fact. Acquiring deep skills in AI and Machine Learning will be an insurance against the risk of obsolescence. Agencies who offers services like these would top the market charts.
Technology is a wave: You can either ride or wipe out
Regardless of your personal stance — luddite or technophile—the rapid advance of technology isn’t going to slow any time soon. More and more small businesses that fail to adapt are going to find themselves left behind while the savvy ones who learn to keep up will reap the rewards.
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